Just as it has become standard practice to write database as one word it is increasingly common in computer science to write knowledgebase as one word (an interim approach was to write the term with a hyphen).

Contents

Types

Knowledge bases are categorized into two major types:

Machine-readable knowledge bases store knowledge in a computer-readable form, usually for the purpose of having automated deductive reasoning applied to them. They contain a set of data, often in the form of rules that describe the knowledge in a logically consistent manner. Logical operators, such as And (conjunction), Or (disjunction), material implication and negation may be used to build it up from the atomic knowledge. Consequently, classical deduction can be used to reason about the knowledge in the knowledge base.

Human-readable knowledge bases are designed to allow people to retrieve and use the knowledge they contain, primarily for training purposes. They are commonly used to capture explicit knowledge of an organization, including troubleshooting, articles, white papers, user manuals and others. A primary benefit of such a knowledge base is that it can help a user to find an existing solution to his or her current problem (thus avoiding having to 're-invent the wheel').

The most important aspect of a knowledge base is the quality of information it contains. The best knowledge bases have carefully written articles that are kept up to date, an excellent information retrieval system (such as a search engine), and a carefully designed content format and classification structure.

A knowledge base may use an ontology to specify its structure (entity types and relationships) and classification scheme. An ontology, together with a set of instances of its classes, constitutes a knowledge base.

Determining what type of information is captured, and where that information resides in a knowledge base, is something that is determined by the processes that support the system. A robust process structure is the backbone of any successful knowledge base.

Some knowledge bases have an artificial intelligence component. These kinds of knowledge bases can suggest solutions to problems sometimes based on feedback provided by the user, and are capable of learning from experience (see expert system). Knowledge representation, automated reasoning and argumentation are active areas of research at the forefront of artificial intelligence.

Implementations

Tufts University School of Medicine has created a software infrastructure called the Tufts University Sciences Knowledgebase, TUSK. It serves as a knowledgebase for curricular information for the health sciences schools at Tufts (medical, dental, veterinary, public health, nutrition, graduate biomedical sciences). This infrastructure has been shared with three medical schools in the U.S., three in Africa and soon, one in India. The infrastructure enables institutions to create a knowledgebase serving local needs.[1]

A pediatric research group at the British Columbia's Children's Hospital has created a knowledge base authoring tool called iKnow. It is used by clinicians at the hospital to develop knowledge rules for improving patient care in surgical operations. A decision support system then runs the rules in the operating room to assist anesthesiologists in dealing with adverse events. The knowledge base developed is machine-readable, and using iKnow it is also human-readable.[2][3]

Teragrid (Project which integrates high-performance computers, data resources and tools, and high-end experimental facilities around the United States of America) has developed its own knowledge base in human readable form. It is a collection of documents which can be retrieved using several interfaces. The TeraGrid Knowledge Base can be searched for content and text. The default interface for the TeraGrid Knowledge base shows document in categories such as most recently viewed, added, edited and most popular. For every document displayed a set of related documents which may interest the user are shown.